# sets the directory of location of this script as the current directory
# setwd(dirname(rstudioapi::getSourceEditorContext()$path))
### load packages
require(pacman)
p_load('tidyverse', 'jsonlite', 'magrittr', 'xlsx',
'stargazer', 'psych', 'jtools', 'DT', 'ggstatsplot',
'lavaan',
'regsem', 'MplusAutomation', 'igraph')
setwd("outputs/01_dataPreperation/final")
### load questionnaire
questionnaire <- readRDS(file = "questionnaire_final.rds")
questionnaireCAMs <- readRDS(file = "questionnaireCAMs_final.rds")
networkIndicators_pre <- readRDS(file = "networkIndicators_pre_final.rds")
networkIndicators_post <- readRDS(file = "networkIndicators_post_final.rds")
CAMfiles_combined <- readRDS(file = "CAMfiles_combined_final.rds")
### load functions
# print(getwd())
setwd("../../../../../functions")
for(i in 1:length(dir())){
# print(dir()[i])
source(dir()[i], encoding = "utf-8")
}
setwd("../functions_CAMapp")
for(i in 1:length(dir())){
# print(dir()[i])
source(dir()[i], encoding = "utf-8")
}
rm(i)
### summary function
data_summary <- function(data, varname, groupnames){
require(plyr)
summary_func <- function(x, col){
c(mean = mean(x[[col]], na.rm=TRUE),
se = sd(x[[col]], na.rm=TRUE) / sqrt(length(x[[col]])))
}
data_sum<-ddply(data, groupnames, .fun=summary_func,
varname)
data_sum <- plyr::rename(data_sum, c("mean" = varname))
return(data_sum)
}Data analyses for S2 main CAM study
Notes
load cleaned data files
check single scales and compute mean variables
Almere
Anxiety dimension
regEx <- "^Almere.*anx$"
nameScale <- "Almere - Anxiety"
nameVariable <- "mean_AlmereAnxiety"
### number of items
sum(str_detect(string = colnames(questionnaireCAMs), pattern = regEx))[1] 4### get correlation plot, descriptives, EFA, CFA
### EFA
tmp <- CFAstats(dataset = questionnaireCAMs, regularExp = regEx, labelLatent = str_remove(string = nameVariable, pattern = "mean_"),
showPlots = TRUE,
computeEFA = TRUE,
computeCFA = TRUE,
computeCFAMplus = FALSE)
descriptive statistics:
Mean SD Median CoeffofVariation Minimum Maximun Lower Quantile
Almere-3anx 2.09 1.05 2 0.50 1 5 1
Almere-1anx 2.78 1.21 3 0.43 1 5 1
Almere-4anx 2.18 1.00 2 0.46 1 5 1
Almere-2anx 2.74 1.26 3 0.46 1 5 1
Upper Quantile Skewness Kurtosis(-3) KS-Test
Almere-3anx 5 0.74 -0.26 0
Almere-1anx 5 0.00 -1.21 0
Almere-4anx 5 0.60 -0.41 0
Almere-2anx 5 0.07 -1.23 0
variables under investigation: Almere3anx Almere1anx Almere4anx Almere2anx
Cronbachs Alpha: 0.76 
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
AlmereAnxiety
Number of components: 2
KMO criteria is to low (< .6) for:
Almere1anx Almere4anx Almere2anx
mean KMO: 0.59
EFA factor loadings (1 factor solution):
Loadings:
MR1
Almere3anx 0.673
Almere1anx 0.754
Almere4anx 0.667
Almere2anx 0.727
MR1
SS loadings 1.995
Proportion Var 0.499
CFA summary and fit statistics:
lavaan 0.6.16 ended normally after 22 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 8
Number of observations 216
Model Test User Model:
Standard Scaled
Test Statistic 173.664 114.866
Degrees of freedom 2 2
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.512
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 370.351 227.540
Degrees of freedom 6 6
P-value 0.000 0.000
Scaling correction factor 1.628
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.529 0.491
Tucker-Lewis Index (TLI) -0.413 -0.528
Robust Comparative Fit Index (CFI) 0.527
Robust Tucker-Lewis Index (TLI) -0.420
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -1225.940 -1225.940
Scaling correction factor 1.149
for the MLR correction
Loglikelihood unrestricted model (H1) -1139.108 -1139.108
Scaling correction factor 1.221
for the MLR correction
Akaike (AIC) 2467.880 2467.880
Bayesian (BIC) 2494.882 2494.882
Sample-size adjusted Bayesian (SABIC) 2469.531 2469.531
Root Mean Square Error of Approximation:
RMSEA 0.630 0.511
90 Percent confidence interval - lower 0.553 0.448
90 Percent confidence interval - upper 0.711 0.577
P-value H_0: RMSEA <= 0.050 0.000 0.000
P-value H_0: RMSEA >= 0.080 1.000 1.000
Robust RMSEA 0.628
90 Percent confidence interval - lower 0.534
90 Percent confidence interval - upper 0.729
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 1.000
Standardized Root Mean Square Residual:
SRMR 0.194 0.194
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
AlmereAnxiety =~
Almere3anx 1.000 0.856 0.817
Almere1anx 0.608 0.162 3.760 0.000 0.521 0.431
Almere4anx 0.952 0.080 11.918 0.000 0.815 0.820
Almere2anx 0.612 0.168 3.642 0.000 0.524 0.418
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Almere3anx 0.366 0.098 3.744 0.000 0.366 0.333
.Almere1anx 1.186 0.126 9.430 0.000 1.186 0.814
.Almere4anx 0.323 0.073 4.399 0.000 0.323 0.327
.Almere2anx 1.297 0.140 9.235 0.000 1.297 0.826
AlmereAnxiety 0.733 0.117 6.248 0.000 1.000 1.000
CFA first 6 Modification Indices:
lhs op rhs mi epc sepc.lv sepc.all sepc.nox
14 Almere1anx ~~ Almere2anx 129.321 1.016 1.016 0.819 0.819
11 Almere3anx ~~ Almere4anx 129.320 2.600 2.600 7.568 7.568
15 Almere4anx ~~ Almere2anx 13.246 -0.302 -0.302 -0.467 -0.467
10 Almere3anx ~~ Almere1anx 13.245 -0.316 -0.316 -0.479 -0.479
13 Almere1anx ~~ Almere4anx 9.696 -0.257 -0.257 -0.416 -0.416
12 Almere3anx ~~ Almere2anx 9.696 -0.272 -0.272 -0.394 -0.394### variable mean
questionnaireCAMs[[nameVariable]] <- questionnaireCAMs %>%
select(matches(regEx)) %>%
rowMeans(na.rm = TRUE)Attitude dimension
regEx <- "^Almere.*att$"
nameScale <- "Almere - Attitude"
nameVariable <- "mean_AlmereAttitude"
### number of items
sum(str_detect(string = colnames(questionnaireCAMs), pattern = regEx))[1] 3### get correlation plot, descriptives, EFA, CFA
### EFA
tmp <- CFAstats(dataset = questionnaireCAMs, regularExp = regEx, labelLatent = str_remove(string = nameVariable, pattern = "mean_"),
showPlots = TRUE,
computeEFA = TRUE,
computeCFA = TRUE,
computeCFAMplus = FALSE)
descriptive statistics:
Mean SD Median CoeffofVariation Minimum Maximun Lower Quantile
Almere-1att 4.06 0.78 4 0.19 1 5 1
Almere-3att 4.35 0.70 4 0.16 1 5 1
Almere-2att 3.73 0.91 4 0.24 1 5 1
Upper Quantile Skewness Kurtosis(-3) KS-Test
Almere-1att 5 -0.92 1.56 0
Almere-3att 5 -1.10 2.07 0
Almere-2att 5 -0.76 0.47 0
variables under investigation: Almere1att Almere3att Almere2att
Cronbachs Alpha: 0.73 
Parallel analysis suggests that the number of factors = 1 and the number of components = 1
AlmereAttitude
Number of components: 1
KMO criteria is to low (< .6) for:
Almere1att
mean KMO: 0.62
EFA factor loadings (1 factor solution):
Loadings:
MR1
Almere1att 1.000
Almere3att 0.681
Almere2att 0.570
MR1
SS loadings 1.788
Proportion Var 0.596
CFA summary and fit statistics:
lavaan 0.6.16 ended normally after 19 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 6
Number of observations 216
Model Test User Model:
Standard Scaled
Test Statistic 0.000 0.000
Degrees of freedom 0 0
Model Test Baseline Model:
Test statistic 154.233 148.594
Degrees of freedom 3 3
P-value 0.000 0.000
Scaling correction factor 1.038
User Model versus Baseline Model:
Comparative Fit Index (CFI) 1.000 1.000
Tucker-Lewis Index (TLI) 1.000 1.000
Robust Comparative Fit Index (CFI) NA
Robust Tucker-Lewis Index (TLI) NA
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -689.854 -689.854
Loglikelihood unrestricted model (H1) -689.854 -689.854
Akaike (AIC) 1391.707 1391.707
Bayesian (BIC) 1411.959 1411.959
Sample-size adjusted Bayesian (SABIC) 1392.946 1392.946
Root Mean Square Error of Approximation:
RMSEA 0.000 NA
90 Percent confidence interval - lower 0.000 NA
90 Percent confidence interval - upper 0.000 NA
P-value H_0: RMSEA <= 0.050 NA NA
P-value H_0: RMSEA >= 0.080 NA NA
Robust RMSEA 0.000
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.000
P-value H_0: Robust RMSEA <= 0.050 NA
P-value H_0: Robust RMSEA >= 0.080 NA
Standardized Root Mean Square Residual:
SRMR 0.000 0.000
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
AlmereAttitude =~
Almere1att 1.000 0.739 0.945
Almere3att 0.571 0.112 5.123 0.000 0.422 0.605
Almere2att 0.673 0.117 5.734 0.000 0.498 0.551
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Almere1att 0.066 0.083 0.794 0.427 0.066 0.107
.Almere3att 0.309 0.052 5.937 0.000 0.309 0.634
.Almere2att 0.569 0.068 8.386 0.000 0.569 0.697
AlmereAttitude 0.546 0.108 5.057 0.000 1.000 1.000
CFA first 6 Modification Indices:
[1] lhs op rhs mi epc sepc.lv sepc.all sepc.nox
<0 Zeilen> (oder row.names mit Länge 0)### variable mean
questionnaireCAMs[[nameVariable]] <- questionnaireCAMs %>%
select(matches(regEx)) %>%
rowMeans(na.rm = TRUE)Li & Wang (2021)
Anthropomorphism dimension
regEx <- "^LiWang.*anthropomorphism$"
nameScale <- "LiWang - Anthropomorphism"
nameVariable <- "mean_LiWangAnthropomorphism"
### number of items
sum(str_detect(string = colnames(questionnaireCAMs), pattern = regEx))[1] 5### get correlation plot, descriptives, EFA, CFA
### EFA
tmp <- CFAstats(dataset = questionnaireCAMs, regularExp = regEx, labelLatent = str_remove(string = nameVariable, pattern = "mean_"),
showPlots = TRUE,
computeEFA = TRUE,
computeCFA = TRUE,
computeCFAMplus = FALSE)
descriptive statistics:
Mean SD Median CoeffofVariation Minimum Maximun
LiWang-5anthropomorphism 3.07 1.68 3 0.55 1 7
LiWang-3anthropomorphism 2.21 1.28 2 0.58 1 6
LiWang-2anthropomorphism 3.12 1.62 3 0.52 1 7
LiWang-1anthropomorphism 2.30 1.42 2 0.62 1 6
LiWang-4anthropomorphism 2.75 1.53 2 0.55 1 7
Lower Quantile Upper Quantile Skewness Kurtosis(-3)
LiWang-5anthropomorphism 1 7 0.36 -0.82
LiWang-3anthropomorphism 1 6 0.78 -0.37
LiWang-2anthropomorphism 1 7 0.20 -1.00
LiWang-1anthropomorphism 1 6 0.89 -0.24
LiWang-4anthropomorphism 1 7 0.41 -0.96
KS-Test
LiWang-5anthropomorphism 0
LiWang-3anthropomorphism 0
LiWang-2anthropomorphism 0
LiWang-1anthropomorphism 0
LiWang-4anthropomorphism 0
variables under investigation: LiWang5anthropomorphism LiWang3anthropomorphism LiWang2anthropomorphism LiWang1anthropomorphism LiWang4anthropomorphism
Cronbachs Alpha: 0.82
Error in if (any(lower > upper)) stop("lower>upper integration limits") :
Fehlender Wert, wo TRUE/FALSE nötig ist
[1] "use instead of polychoric correlations pearson correlations"
Parallel analysis suggests that the number of factors = 1 and the number of components = 1
LiWangAnthropomorphism
Number of components: 1
Error in if (any(lower > upper)) stop("lower>upper integration limits") :
Fehlender Wert, wo TRUE/FALSE nötig ist
[1] "use instead of polychoric correlations pearson correlations"
EFA factor loadings (1 factor solution):
Loadings:
MR1
LiWang5anthropomorphism 0.534
LiWang3anthropomorphism 0.828
LiWang2anthropomorphism 0.519
LiWang1anthropomorphism 0.847
LiWang4anthropomorphism 0.763
MR1
SS loadings 2.541
Proportion Var 0.508
CFA summary and fit statistics:
lavaan 0.6.16 ended normally after 28 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 10
Number of observations 216
Model Test User Model:
Standard Scaled
Test Statistic 18.074 12.462
Degrees of freedom 5 5
P-value (Chi-square) 0.003 0.029
Scaling correction factor 1.450
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 431.294 246.390
Degrees of freedom 10 10
P-value 0.000 0.000
Scaling correction factor 1.750
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.969 0.968
Tucker-Lewis Index (TLI) 0.938 0.937
Robust Comparative Fit Index (CFI) 0.974
Robust Tucker-Lewis Index (TLI) 0.948
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -1760.394 -1760.394
Scaling correction factor 1.356
for the MLR correction
Loglikelihood unrestricted model (H1) -1751.357 -1751.357
Scaling correction factor 1.388
for the MLR correction
Akaike (AIC) 3540.788 3540.788
Bayesian (BIC) 3574.541 3574.541
Sample-size adjusted Bayesian (SABIC) 3542.852 3542.852
Root Mean Square Error of Approximation:
RMSEA 0.110 0.083
90 Percent confidence interval - lower 0.058 0.035
90 Percent confidence interval - upper 0.167 0.132
P-value H_0: RMSEA <= 0.050 0.031 0.112
P-value H_0: RMSEA >= 0.080 0.847 0.591
Robust RMSEA 0.100
90 Percent confidence interval - lower 0.030
90 Percent confidence interval - upper 0.171
P-value H_0: Robust RMSEA <= 0.050 0.101
P-value H_0: Robust RMSEA >= 0.080 0.733
Standardized Root Mean Square Residual:
SRMR 0.038 0.038
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv
LiWangAnthropomorphism =~
LWng5nthrpmrph 1.000 0.853
LWng3nthrpmrph 1.275 0.158 8.074 0.000 1.088
LWng2nthrpmrph 0.966 0.157 6.165 0.000 0.824
LWng1nthrpmrph 1.443 0.188 7.658 0.000 1.231
LWng4nthrpmrph 1.302 0.161 8.089 0.000 1.111
Std.all
0.510
0.852
0.508
0.869
0.729
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.LWng5nthrpmrph 2.072 0.250 8.297 0.000 2.072 0.740
.LWng3nthrpmrph 0.447 0.101 4.425 0.000 0.447 0.274
.LWng2nthrpmrph 1.949 0.205 9.496 0.000 1.949 0.741
.LWng1nthrpmrph 0.490 0.112 4.391 0.000 0.490 0.244
.LWng4nthrpmrph 1.089 0.198 5.515 0.000 1.089 0.469
LWngAnthrpmrph 0.728 0.180 4.034 0.000 1.000 1.000
CFA first 6 Modification Indices:
lhs op rhs mi epc sepc.lv
17 LiWang3anthropomorphism ~~ LiWang1anthropomorphism 10.596 0.381 0.381
14 LiWang5anthropomorphism ~~ LiWang1anthropomorphism 9.066 -0.301 -0.301
15 LiWang5anthropomorphism ~~ LiWang4anthropomorphism 8.305 0.334 0.334
16 LiWang3anthropomorphism ~~ LiWang2anthropomorphism 6.031 -0.214 -0.214
21 LiWang1anthropomorphism ~~ LiWang4anthropomorphism 1.804 -0.144 -0.144
18 LiWang3anthropomorphism ~~ LiWang4anthropomorphism 1.770 -0.125 -0.125
sepc.all sepc.nox
17 0.814 0.814
14 -0.299 -0.299
15 0.222 0.222
16 -0.230 -0.230
21 -0.197 -0.197
18 -0.179 -0.179### variable mean
questionnaireCAMs[[nameVariable]] <- questionnaireCAMs %>%
select(matches(regEx)) %>%
rowMeans(na.rm = TRUE)Autonomy dimension
regEx <- "^LiWang.*autonomy$"
nameScale <- "LiWang - Autonomy"
nameVariable <- "mean_LiWangAutonomy"
### number of items
sum(str_detect(string = colnames(questionnaireCAMs), pattern = regEx))[1] 3### get correlation plot, descriptives, EFA, CFA
### EFA
tmp <- CFAstats(dataset = questionnaireCAMs, regularExp = regEx, labelLatent = str_remove(string = nameVariable, pattern = "mean_"),
showPlots = TRUE,
computeEFA = TRUE,
computeCFA = TRUE,
computeCFAMplus = FALSE)
descriptive statistics:
Mean SD Median CoeffofVariation Minimum Maximun
LiWang-3autonomy 5.31 1.30 6 0.25 1 7
LiWang-1autonomy 5.59 1.31 6 0.23 1 7
LiWang-2autonomy 4.29 1.57 5 0.37 1 7
Lower Quantile Upper Quantile Skewness Kurtosis(-3) KS-Test
LiWang-3autonomy 1 7 -1.02 1.15 0
LiWang-1autonomy 1 7 -1.43 2.66 0
LiWang-2autonomy 1 7 -0.48 -0.52 0
variables under investigation: LiWang3autonomy LiWang1autonomy LiWang2autonomy
Cronbachs Alpha: 0.66 
Parallel analysis suggests that the number of factors = 1 and the number of components = 1
LiWangAutonomy
Number of components: 1
EFA factor loadings (1 factor solution):
Loadings:
MR1
LiWang3autonomy 0.710
LiWang1autonomy 0.539
LiWang2autonomy 0.686
MR1
SS loadings 1.266
Proportion Var 0.422
CFA summary and fit statistics:
lavaan 0.6.16 ended normally after 24 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 6
Number of observations 216
Model Test User Model:
Standard Scaled
Test Statistic 0.000 0.000
Degrees of freedom 0 0
Model Test Baseline Model:
Test statistic 92.541 56.237
Degrees of freedom 3 3
P-value 0.000 0.000
Scaling correction factor 1.646
User Model versus Baseline Model:
Comparative Fit Index (CFI) 1.000 1.000
Tucker-Lewis Index (TLI) 1.000 1.000
Robust Comparative Fit Index (CFI) NA
Robust Tucker-Lewis Index (TLI) NA
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -1084.868 -1084.868
Loglikelihood unrestricted model (H1) -1084.868 -1084.868
Akaike (AIC) 2181.737 2181.737
Bayesian (BIC) 2201.988 2201.988
Sample-size adjusted Bayesian (SABIC) 2182.975 2182.975
Root Mean Square Error of Approximation:
RMSEA 0.000 NA
90 Percent confidence interval - lower 0.000 NA
90 Percent confidence interval - upper 0.000 NA
P-value H_0: RMSEA <= 0.050 NA NA
P-value H_0: RMSEA >= 0.080 NA NA
Robust RMSEA 0.000
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.000
P-value H_0: Robust RMSEA <= 0.050 NA
P-value H_0: Robust RMSEA >= 0.080 NA
Standardized Root Mean Square Residual:
SRMR 0.000 0.000
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
LiWangAutonomy =~
LiWang3autonmy 1.000 0.886 0.682
LiWang1autonmy 0.821 0.179 4.589 0.000 0.728 0.556
LiWang2autonmy 1.146 0.269 4.263 0.000 1.016 0.648
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.LiWang3autonmy 0.901 0.269 3.353 0.001 0.901 0.534
.LiWang1autonmy 1.184 0.193 6.144 0.000 1.184 0.691
.LiWang2autonmy 1.425 0.244 5.827 0.000 1.425 0.580
LiWangAutonomy 0.785 0.281 2.797 0.005 1.000 1.000
CFA first 6 Modification Indices:
[1] lhs op rhs mi epc sepc.lv sepc.all sepc.nox
<0 Zeilen> (oder row.names mit Länge 0)### variable mean
questionnaireCAMs[[nameVariable]] <- questionnaireCAMs %>%
select(matches(regEx)) %>%
rowMeans(na.rm = TRUE)General Attitudes Towards Robots Scale, GAToRS (2022)
Personal Level Positive Attitude
regEx <- "^GAToRS.*pp$"
nameScale <- "GAToRS - PP"
nameVariable <- "mean_GAToRSpp"
### number of items
sum(str_detect(string = colnames(questionnaireCAMs), pattern = regEx))[1] 5### get correlation plot, descriptives, EFA, CFA
### EFA
tmp <- CFAstats(dataset = questionnaireCAMs, regularExp = regEx, labelLatent = str_remove(string = nameVariable, pattern = "mean_"),
showPlots = TRUE,
computeEFA = TRUE,
computeCFA = TRUE,
computeCFAMplus = FALSE)
descriptive statistics:
Mean SD Median CoeffofVariation Minimum Maximun Lower Quantile
GAToRS-4pp 4.27 1.51 4 0.35 1 7 1
GAToRS-3pp 4.18 1.32 4 0.32 1 7 1
GAToRS-5pp 4.04 1.63 4 0.40 1 7 1
GAToRS-2pp 4.41 1.42 5 0.32 1 7 1
GAToRS-1pp 4.30 1.29 4 0.30 1 7 1
Upper Quantile Skewness Kurtosis(-3) KS-Test
GAToRS-4pp 7 -0.19 -0.66 0
GAToRS-3pp 7 -0.54 0.35 0
GAToRS-5pp 7 -0.21 -0.77 0
GAToRS-2pp 7 -0.37 -0.44 0
GAToRS-1pp 7 -0.43 0.17 0
variables under investigation: GAToRS4pp GAToRS3pp GAToRS5pp GAToRS2pp GAToRS1pp
Cronbachs Alpha: 0.69 
Parallel analysis suggests that the number of factors = 2 and the number of components = 1
GAToRSpp
Number of components: 1
EFA factor loadings (1 factor solution):
Loadings:
MR1
GAToRS4pp 0.624
GAToRS3pp 0.713
GAToRS5pp 0.345
GAToRS2pp 0.591
GAToRS1pp 0.617
MR1
SS loadings 1.746
Proportion Var 0.349
CFA summary and fit statistics:
lavaan 0.6.16 ended normally after 29 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 10
Number of observations 216
Model Test User Model:
Standard Scaled
Test Statistic 37.667 40.165
Degrees of freedom 5 5
P-value (Chi-square) 0.000 0.000
Scaling correction factor 0.938
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 209.654 174.056
Degrees of freedom 10 10
P-value 0.000 0.000
Scaling correction factor 1.205
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.836 0.786
Tucker-Lewis Index (TLI) 0.673 0.571
Robust Comparative Fit Index (CFI) 0.833
Robust Tucker-Lewis Index (TLI) 0.666
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -1831.716 -1831.716
Scaling correction factor 1.168
for the MLR correction
Loglikelihood unrestricted model (H1) -1812.882 -1812.882
Scaling correction factor 1.091
for the MLR correction
Akaike (AIC) 3683.431 3683.431
Bayesian (BIC) 3717.184 3717.184
Sample-size adjusted Bayesian (SABIC) 3685.496 3685.496
Root Mean Square Error of Approximation:
RMSEA 0.174 0.180
90 Percent confidence interval - lower 0.125 0.130
90 Percent confidence interval - upper 0.228 0.236
P-value H_0: RMSEA <= 0.050 0.000 0.000
P-value H_0: RMSEA >= 0.080 0.999 0.999
Robust RMSEA 0.175
90 Percent confidence interval - lower 0.127
90 Percent confidence interval - upper 0.227
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 0.999
Standardized Root Mean Square Residual:
SRMR 0.075 0.075
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
GAToRSpp =~
GAToRS4pp 1.000 0.883 0.585
GAToRS3pp 1.003 0.140 7.191 0.000 0.886 0.670
GAToRS5pp 0.547 0.188 2.919 0.004 0.483 0.297
GAToRS2pp 0.967 0.196 4.941 0.000 0.853 0.601
GAToRS1pp 0.960 0.210 4.571 0.000 0.848 0.657
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.GAToRS4pp 1.501 0.186 8.073 0.000 1.501 0.658
.GAToRS3pp 0.962 0.190 5.062 0.000 0.962 0.551
.GAToRS5pp 2.413 0.244 9.889 0.000 2.413 0.912
.GAToRS2pp 1.291 0.203 6.365 0.000 1.291 0.639
.GAToRS1pp 0.945 0.170 5.545 0.000 0.945 0.568
GAToRSpp 0.779 0.198 3.930 0.000 1.000 1.000
CFA first 6 Modification Indices:
lhs op rhs mi epc sepc.lv sepc.all sepc.nox
20 GAToRS5pp ~~ GAToRS1pp 16.774 -0.518 -0.518 -0.343 -0.343
21 GAToRS2pp ~~ GAToRS1pp 15.266 0.487 0.487 0.441 0.441
17 GAToRS3pp ~~ GAToRS2pp 13.196 -0.472 -0.472 -0.423 -0.423
13 GAToRS4pp ~~ GAToRS5pp 12.423 0.525 0.525 0.276 0.276
15 GAToRS4pp ~~ GAToRS1pp 9.735 -0.405 -0.405 -0.340 -0.340
16 GAToRS3pp ~~ GAToRS5pp 3.438 0.241 0.241 0.158 0.158### variable mean
questionnaireCAMs[[nameVariable]] <- questionnaireCAMs %>%
select(matches(regEx)) %>%
rowMeans(na.rm = TRUE)Personal Level Negative Attitude
regEx <- "^GAToRS.*pn$"
nameScale <- "GAToRS - pn"
nameVariable <- "mean_GAToRSpn"
### number of items
sum(str_detect(string = colnames(questionnaireCAMs), pattern = regEx))[1] 5### get correlation plot, descriptives, EFA, CFA
### EFA
tmp <- CFAstats(dataset = questionnaireCAMs, regularExp = regEx, labelLatent = str_remove(string = nameVariable, pattern = "mean_"),
showPlots = TRUE,
computeEFA = TRUE,
computeCFA = TRUE,
computeCFAMplus = FALSE)
descriptive statistics:
Mean SD Median CoeffofVariation Minimum Maximun Lower Quantile
GAToRS-2pn 3.78 1.50 4 0.40 1 7 1
GAToRS-4pn 2.77 1.47 3 0.53 1 7 1
GAToRS-3pn 2.56 1.41 2 0.55 1 7 1
GAToRS-1pn 2.88 1.52 3 0.53 1 7 1
GAToRS-5pn 3.27 1.70 3 0.52 1 7 1
Upper Quantile Skewness Kurtosis(-3) KS-Test
GAToRS-2pn 7 0.12 -0.59 0
GAToRS-4pn 7 0.82 0.26 0
GAToRS-3pn 7 0.83 0.22 0
GAToRS-1pn 7 0.64 -0.38 0
GAToRS-5pn 7 0.38 -0.75 0
variables under investigation: GAToRS2pn GAToRS4pn GAToRS3pn GAToRS1pn GAToRS5pn
Cronbachs Alpha: 0.82
Error in if (any(lower > upper)) stop("lower>upper integration limits") :
Fehlender Wert, wo TRUE/FALSE nötig ist
[1] "use instead of polychoric correlations pearson correlations"
Parallel analysis suggests that the number of factors = 1 and the number of components = 1
GAToRSpn
Number of components: 1
EFA factor loadings (1 factor solution):
Loadings:
MR1
GAToRS2pn 0.560
GAToRS4pn 0.769
GAToRS3pn 0.796
GAToRS1pn 0.720
GAToRS5pn 0.745
MR1
SS loadings 2.612
Proportion Var 0.522
CFA summary and fit statistics:
lavaan 0.6.16 ended normally after 24 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 10
Number of observations 216
Model Test User Model:
Standard Scaled
Test Statistic 4.864 4.224
Degrees of freedom 5 5
P-value (Chi-square) 0.433 0.518
Scaling correction factor 1.152
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 343.042 252.792
Degrees of freedom 10 10
P-value 0.000 0.000
Scaling correction factor 1.357
User Model versus Baseline Model:
Comparative Fit Index (CFI) 1.000 1.000
Tucker-Lewis Index (TLI) 1.001 1.006
Robust Comparative Fit Index (CFI) 1.000
Robust Tucker-Lewis Index (TLI) 1.005
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -1811.124 -1811.124
Scaling correction factor 1.219
for the MLR correction
Loglikelihood unrestricted model (H1) -1808.692 -1808.692
Scaling correction factor 1.197
for the MLR correction
Akaike (AIC) 3642.248 3642.248
Bayesian (BIC) 3676.000 3676.000
Sample-size adjusted Bayesian (SABIC) 3644.312 3644.312
Root Mean Square Error of Approximation:
RMSEA 0.000 0.000
90 Percent confidence interval - lower 0.000 0.000
90 Percent confidence interval - upper 0.093 0.082
P-value H_0: RMSEA <= 0.050 0.696 0.784
P-value H_0: RMSEA >= 0.080 0.101 0.056
Robust RMSEA 0.000
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.093
P-value H_0: Robust RMSEA <= 0.050 0.736
P-value H_0: Robust RMSEA >= 0.080 0.095
Standardized Root Mean Square Residual:
SRMR 0.020 0.020
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
GAToRSpn =~
GAToRS2pn 1.000 0.801 0.535
GAToRS4pn 1.351 0.188 7.197 0.000 1.082 0.740
GAToRS3pn 1.344 0.191 7.037 0.000 1.077 0.764
GAToRS1pn 1.285 0.212 6.061 0.000 1.030 0.680
GAToRS5pn 1.519 0.188 8.067 0.000 1.217 0.717
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.GAToRS2pn 1.603 0.161 9.935 0.000 1.603 0.714
.GAToRS4pn 0.970 0.162 5.991 0.000 0.970 0.453
.GAToRS3pn 0.828 0.123 6.735 0.000 0.828 0.417
.GAToRS1pn 1.235 0.213 5.784 0.000 1.235 0.538
.GAToRS5pn 1.403 0.206 6.796 0.000 1.403 0.486
GAToRSpn 0.642 0.171 3.748 0.000 1.000 1.000
CFA first 6 Modification Indices:
lhs op rhs mi epc sepc.lv sepc.all sepc.nox
16 GAToRS4pn ~~ GAToRS3pn 4.303 0.225 0.225 0.251 0.251
18 GAToRS4pn ~~ GAToRS5pn 1.856 -0.171 -0.171 -0.146 -0.146
13 GAToRS2pn ~~ GAToRS3pn 1.754 -0.137 -0.137 -0.119 -0.119
21 GAToRS1pn ~~ GAToRS5pn 1.151 0.135 0.135 0.103 0.103
15 GAToRS2pn ~~ GAToRS5pn 0.834 0.115 0.115 0.077 0.077
19 GAToRS3pn ~~ GAToRS1pn 0.579 -0.081 -0.081 -0.080 -0.080### variable mean
questionnaireCAMs[[nameVariable]] <- questionnaireCAMs %>%
select(matches(regEx)) %>%
rowMeans(na.rm = TRUE)Societal Level Positive Attitude
regEx <- "^GAToRS.*sp$"
nameScale <- "GAToRS - sp"
nameVariable <- "mean_GAToRSsp"
### number of items
sum(str_detect(string = colnames(questionnaireCAMs), pattern = regEx))[1] 5### get correlation plot, descriptives, EFA, CFA
### EFA
tmp <- CFAstats(dataset = questionnaireCAMs, regularExp = regEx, labelLatent = str_remove(string = nameVariable, pattern = "mean_"),
showPlots = TRUE,
computeEFA = TRUE,
computeCFA = TRUE,
computeCFAMplus = FALSE)
descriptive statistics:
Mean SD Median CoeffofVariation Minimum Maximun Lower Quantile
GAToRS-3sp 5.74 1.22 6 0.21 1 7 1
GAToRS-2sp 6.14 0.94 6 0.15 2 7 2
GAToRS-1sp 6.04 1.05 6 0.17 2 7 2
GAToRS-4sp 5.43 1.36 6 0.25 1 7 1
GAToRS-5sp 5.49 1.10 6 0.20 1 7 1
Upper Quantile Skewness Kurtosis(-3) KS-Test
GAToRS-3sp 7 -1.09 1.09 0
GAToRS-2sp 7 -1.32 2.34 0
GAToRS-1sp 7 -1.27 1.76 0
GAToRS-4sp 7 -1.01 0.97 0
GAToRS-5sp 7 -1.06 1.89 0
variables under investigation: GAToRS3sp GAToRS2sp GAToRS1sp GAToRS4sp GAToRS5sp
Cronbachs Alpha: 0.8 
Parallel analysis suggests that the number of factors = 2 and the number of components = 1
GAToRSsp
Number of components: 1
EFA factor loadings (1 factor solution):
Loadings:
MR1
GAToRS3sp 0.681
GAToRS2sp 0.826
GAToRS1sp 0.749
GAToRS4sp 0.532
GAToRS5sp 0.712
MR1
SS loadings 2.498
Proportion Var 0.500
CFA summary and fit statistics:
lavaan 0.6.16 ended normally after 22 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 10
Number of observations 216
Model Test User Model:
Standard Scaled
Test Statistic 10.602 10.107
Degrees of freedom 5 5
P-value (Chi-square) 0.060 0.072
Scaling correction factor 1.049
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 316.156 251.157
Degrees of freedom 10 10
P-value 0.000 0.000
Scaling correction factor 1.259
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.982 0.979
Tucker-Lewis Index (TLI) 0.963 0.958
Robust Comparative Fit Index (CFI) 0.982
Robust Tucker-Lewis Index (TLI) 0.965
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -1504.582 -1504.582
Scaling correction factor 1.637
for the MLR correction
Loglikelihood unrestricted model (H1) -1499.281 -1499.281
Scaling correction factor 1.441
for the MLR correction
Akaike (AIC) 3029.164 3029.164
Bayesian (BIC) 3062.916 3062.916
Sample-size adjusted Bayesian (SABIC) 3031.228 3031.228
Root Mean Square Error of Approximation:
RMSEA 0.072 0.069
90 Percent confidence interval - lower 0.000 0.000
90 Percent confidence interval - upper 0.133 0.129
P-value H_0: RMSEA <= 0.050 0.226 0.251
P-value H_0: RMSEA >= 0.080 0.474 0.435
Robust RMSEA 0.070
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.133
P-value H_0: Robust RMSEA <= 0.050 0.244
P-value H_0: Robust RMSEA >= 0.080 0.461
Standardized Root Mean Square Residual:
SRMR 0.033 0.033
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
GAToRSsp =~
GAToRS3sp 1.000 0.770 0.632
GAToRS2sp 0.972 0.155 6.251 0.000 0.748 0.798
GAToRS1sp 0.929 0.154 6.039 0.000 0.715 0.683
GAToRS4sp 0.887 0.164 5.412 0.000 0.682 0.503
GAToRS5sp 1.018 0.165 6.177 0.000 0.783 0.711
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.GAToRS3sp 0.889 0.154 5.777 0.000 0.889 0.600
.GAToRS2sp 0.320 0.057 5.635 0.000 0.320 0.364
.GAToRS1sp 0.584 0.096 6.087 0.000 0.584 0.533
.GAToRS4sp 1.372 0.181 7.574 0.000 1.372 0.747
.GAToRS5sp 0.600 0.090 6.632 0.000 0.600 0.494
GAToRSsp 0.592 0.171 3.463 0.001 1.000 1.000
CFA first 6 Modification Indices:
lhs op rhs mi epc sepc.lv sepc.all sepc.nox
19 GAToRS1sp ~~ GAToRS4sp 7.371 0.199 0.199 0.222 0.222
18 GAToRS2sp ~~ GAToRS5sp 6.055 0.149 0.149 0.340 0.340
16 GAToRS2sp ~~ GAToRS1sp 3.948 -0.110 -0.110 -0.255 -0.255
21 GAToRS4sp ~~ GAToRS5sp 3.480 -0.143 -0.143 -0.157 -0.157
15 GAToRS3sp ~~ GAToRS5sp 0.634 -0.054 -0.054 -0.075 -0.075
17 GAToRS2sp ~~ GAToRS4sp 0.579 -0.050 -0.050 -0.075 -0.075### variable mean
questionnaireCAMs[[nameVariable]] <- questionnaireCAMs %>%
select(matches(regEx)) %>%
rowMeans(na.rm = TRUE)Societal Level Negative Attitude
regEx <- "^GAToRS.*sn$"
nameScale <- "GAToRS - sn"
nameVariable <- "mean_GAToRSsn"
### number of items
sum(str_detect(string = colnames(questionnaireCAMs), pattern = regEx))[1] 5### get correlation plot, descriptives, EFA, CFA
### EFA
tmp <- CFAstats(dataset = questionnaireCAMs, regularExp = regEx, labelLatent = str_remove(string = nameVariable, pattern = "mean_"),
showPlots = TRUE,
computeEFA = TRUE,
computeCFA = TRUE,
computeCFAMplus = FALSE)
descriptive statistics:
Mean SD Median CoeffofVariation Minimum Maximun Lower Quantile
GAToRS-5sn 5.36 1.39 6 0.26 1 7 1
GAToRS-1sn 4.94 1.65 5 0.33 1 7 1
GAToRS-3sn 4.52 1.72 5 0.38 1 7 1
GAToRS-4sn 5.44 1.30 5 0.24 1 7 1
GAToRS-2sn 4.77 1.53 5 0.32 1 7 1
Upper Quantile Skewness Kurtosis(-3) KS-Test
GAToRS-5sn 7 -0.91 0.60 0
GAToRS-1sn 7 -0.62 -0.51 0
GAToRS-3sn 7 -0.44 -0.75 0
GAToRS-4sn 7 -0.72 0.30 0
GAToRS-2sn 7 -0.54 -0.35 0
variables under investigation: GAToRS5sn GAToRS1sn GAToRS3sn GAToRS4sn GAToRS2sn
Cronbachs Alpha: 0.62 
Parallel analysis suggests that the number of factors = 2 and the number of components = 1
GAToRSsn
Number of components: 1
KMO criteria is to low (< .6) for:
GAToRS4sn
mean KMO: 0.64
EFA factor loadings (1 factor solution):
Loadings:
MR1
GAToRS5sn 0.550
GAToRS1sn 0.610
GAToRS3sn 0.478
GAToRS4sn 0.450
GAToRS2sn 0.509
MR1
SS loadings 1.363
Proportion Var 0.273
CFA summary and fit statistics:
lavaan 0.6.16 ended normally after 38 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 10
Number of observations 216
Model Test User Model:
Standard Scaled
Test Statistic 33.232 52.763
Degrees of freedom 5 5
P-value (Chi-square) 0.000 0.000
Scaling correction factor 0.630
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 129.470 100.031
Degrees of freedom 10 10
P-value 0.000 0.000
Scaling correction factor 1.294
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.764 0.469
Tucker-Lewis Index (TLI) 0.527 -0.061
Robust Comparative Fit Index (CFI) 0.742
Robust Tucker-Lewis Index (TLI) 0.484
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -1926.353 -1926.353
Scaling correction factor 1.444
for the MLR correction
Loglikelihood unrestricted model (H1) -1909.737 -1909.737
Scaling correction factor 1.173
for the MLR correction
Akaike (AIC) 3872.705 3872.705
Bayesian (BIC) 3906.458 3906.458
Sample-size adjusted Bayesian (SABIC) 3874.770 3874.770
Root Mean Square Error of Approximation:
RMSEA 0.162 0.210
90 Percent confidence interval - lower 0.112 0.149
90 Percent confidence interval - upper 0.216 0.278
P-value H_0: RMSEA <= 0.050 0.000 0.000
P-value H_0: RMSEA >= 0.080 0.996 1.000
Robust RMSEA 0.167
90 Percent confidence interval - lower 0.128
90 Percent confidence interval - upper 0.209
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 1.000
Standardized Root Mean Square Residual:
SRMR 0.080 0.080
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
GAToRSsn =~
GAToRS5sn 1.000 0.657 0.475
GAToRS1sn 1.484 0.964 1.539 0.124 0.975 0.592
GAToRS3sn 1.228 0.591 2.079 0.038 0.807 0.470
GAToRS4sn 0.769 0.167 4.610 0.000 0.506 0.390
GAToRS2sn 1.234 0.788 1.566 0.117 0.811 0.532
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.GAToRS5sn 1.482 0.334 4.441 0.000 1.482 0.774
.GAToRS1sn 1.767 0.475 3.722 0.000 1.767 0.650
.GAToRS3sn 2.292 0.280 8.180 0.000 2.292 0.779
.GAToRS4sn 1.425 0.275 5.184 0.000 1.425 0.848
.GAToRS2sn 1.668 0.331 5.044 0.000 1.668 0.717
GAToRSsn 0.432 0.357 1.212 0.225 1.000 1.000
CFA first 6 Modification Indices:
lhs op rhs mi epc sepc.lv sepc.all sepc.nox
14 GAToRS5sn ~~ GAToRS4sn 30.084 0.664 0.664 0.457 0.457
18 GAToRS1sn ~~ GAToRS2sn 12.184 0.750 0.750 0.437 0.437
12 GAToRS5sn ~~ GAToRS1sn 8.487 -0.523 -0.523 -0.323 -0.323
21 GAToRS4sn ~~ GAToRS2sn 8.255 -0.394 -0.394 -0.255 -0.255
17 GAToRS1sn ~~ GAToRS4sn 3.069 -0.271 -0.271 -0.171 -0.171
15 GAToRS5sn ~~ GAToRS2sn 0.925 -0.149 -0.149 -0.095 -0.095### variable mean
questionnaireCAMs[[nameVariable]] <- questionnaireCAMs %>%
select(matches(regEx)) %>%
rowMeans(na.rm = TRUE)Draw CAMs and compute network indicators
### draw CAMs
CAMdrawn <- draw_CAM(dat_merged = CAMfiles_combined[[3]],
dat_nodes = CAMfiles_combined[[1]],ids_CAMs = "all",
plot_CAM = FALSE,
useCoordinates = TRUE,
relvertexsize = 3,
reledgesize = 1)processing 432 CAMs...
[1] "== ids_CAMs in drawnCAM"### network indicators
tmp_microIndicator <- c("Rettungsroboter", "sozialer Assistenzroboter", "Vorteile", "Nachteile")
networkIndicators <- compute_indicatorsCAM(drawn_CAM = CAMdrawn,
micro_degree = tmp_microIndicator,
micro_valence = tmp_microIndicator,
micro_centr_clo = tmp_microIndicator,
micro_transitivity = tmp_microIndicator,
largestClique = FALSE)
### wordlists
CAMwordlist <- create_wordlist(
dat_nodes = CAMfiles_combined[[1]],
dat_merged = CAMfiles_combined[[3]],
useSummarized = TRUE,
order = "frequency",
splitByValence = FALSE,
comments = TRUE,
raterSubsetWords = NULL,
rater = FALSE
)[1] "create_wordlist - use summarized words"
[1] 6404
[1] 6404
processing 432 CAMs...
[1] "== ids_CAMs in drawnCAM"DT::datatable(CAMwordlist, options = list(pageLength = 5)) Descriptive Analyses
describe sample
psych::describe(questionnaireCAMs[, c("socio_age")]) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 215 30.19 8.55 29 29.03 7.41 18 67 49 1.66 3.6 0.58table(questionnaireCAMs$socio_sex)
Female Male
75 141 table(questionnaireCAMs$socio_student)
No Yes
113 86 table(questionnaireCAMs$socio_employment)
Due to start a new job within the next month
4
Full-Time
84
Not in paid work (e.g. homemaker', 'retired or disabled)
7
Other
20
Part-Time
56
Unemployed (and job seeking)
19 ## split by robot
psych::describe(socio_age ~ choosen_Robot, data = questionnaireCAMs)
Descriptive statistics by group
choosen_Robot: Rettungsroboter
vars n mean sd median trimmed mad min max range skew kurtosis
socio_age 1 117 30.09 8.29 29 29.19 7.41 18 63 45 1.48 3.2
se
socio_age 0.77
------------------------------------------------------------
choosen_Robot: sozialer Assistenzroboter
vars n mean sd median trimmed mad min max range skew kurtosis se
socio_age 1 98 30.31 8.9 28.5 28.91 6.67 18 67 49 1.8 3.74 0.9table(questionnaireCAMs$socio_sex, questionnaireCAMs$choosen_Robot)
Rettungsroboter sozialer Assistenzroboter
Female 46 29
Male 71 70feedback to the study
Question: Haben Sie Feedback oder Kritik an der Online-Studie?
DT::datatable(questionnaireCAMs[,c("PROLIFIC_PID", "feedback_critic")], options = list(pageLength = 5)) technical problems CAMEL
DT::datatable(questionnaireCAMs[,c("PROLIFIC_PID", str_subset(string = colnames(questionnaireCAMs), pattern = "^feedCAM"))], options = list(pageLength = 5)) hist(questionnaire$feedCAM_repres)
summary(questionnaire$feedCAM_repres) Min. 1st Qu. Median Mean 3rd Qu. Max.
2.000 6.000 6.000 6.023 7.000 7.000 differences survey means
## split by robot
psych::describe(mean_AlmereAnxiety + mean_AlmereAttitude + mean_LiWangAnthropomorphism + mean_LiWangAutonomy + mean_GAToRSpp + mean_GAToRSpn + mean_GAToRSsp + mean_GAToRSsn ~ choosen_Robot, data = questionnaireCAMs)
Descriptive statistics by group
choosen_Robot: Rettungsroboter
vars n mean sd median trimmed mad min max
mean_AlmereAnxiety 1 117 2.57 0.87 2.75 2.58 0.74 1.00 5.0
mean_AlmereAttitude 2 117 4.16 0.62 4.33 4.21 0.49 1.67 5.0
mean_LiWangAnthropomorphism 3 117 2.59 1.14 2.40 2.54 1.48 1.00 5.0
mean_LiWangAutonomy 4 117 4.96 1.09 5.00 5.02 0.99 1.00 7.0
mean_GAToRSpp 5 117 4.29 1.02 4.40 4.33 0.89 1.00 7.0
mean_GAToRSpn 6 117 2.96 1.07 3.00 2.92 1.19 1.00 6.4
mean_GAToRSsp 7 117 5.78 0.90 6.00 5.89 0.59 2.20 7.0
mean_GAToRSsn 8 117 4.89 1.03 5.00 4.93 0.89 2.20 7.0
range skew kurtosis se
mean_AlmereAnxiety 4.00 -0.05 -0.33 0.08
mean_AlmereAttitude 3.33 -1.17 2.11 0.06
mean_LiWangAnthropomorphism 4.00 0.30 -0.96 0.11
mean_LiWangAutonomy 6.00 -0.80 1.42 0.10
mean_GAToRSpp 6.00 -0.46 0.46 0.09
mean_GAToRSpn 5.40 0.39 0.19 0.10
mean_GAToRSsp 4.80 -1.39 2.46 0.08
mean_GAToRSsn 4.80 -0.37 -0.25 0.10
------------------------------------------------------------
choosen_Robot: sozialer Assistenzroboter
vars n mean sd median trimmed mad min max
mean_AlmereAnxiety 1 99 2.30 0.83 2.25 2.29 1.11 1.0 4.00
mean_AlmereAttitude 2 99 3.92 0.65 4.00 3.96 0.49 1.0 5.00
mean_LiWangAnthropomorphism 3 99 2.82 1.15 2.80 2.77 1.19 1.0 5.60
mean_LiWangAutonomy 4 99 5.19 1.06 5.67 5.30 0.99 1.0 6.67
mean_GAToRSpp 5 99 4.18 0.88 4.20 4.21 0.89 1.8 5.80
mean_GAToRSpn 6 99 3.16 1.25 3.00 3.09 1.19 1.0 6.60
mean_GAToRSsp 7 99 5.75 0.76 5.80 5.82 0.59 3.4 7.00
mean_GAToRSsn 8 99 5.13 0.84 5.20 5.15 0.89 3.4 6.80
range skew kurtosis se
mean_AlmereAnxiety 3.00 0.16 -1.14 0.08
mean_AlmereAttitude 4.00 -1.11 3.12 0.06
mean_LiWangAnthropomorphism 4.60 0.28 -0.64 0.12
mean_LiWangAutonomy 5.67 -1.19 1.88 0.11
mean_GAToRSpp 4.00 -0.36 -0.18 0.09
mean_GAToRSpn 5.60 0.60 -0.04 0.13
mean_GAToRSsp 3.60 -0.88 1.06 0.08
mean_GAToRSsn 3.40 -0.21 -0.76 0.08Analyses
Quantitative bird’s eye view
survey scales
Check for mean differences between robots
## mean_AlmereAnxiety
tmp <- aov(formula = mean_AlmereAnxiety ~ choosen_Robot, data = questionnaireCAMs)
if(summary(tmp)[[1]][["Pr(>F)"]][1] < .05){
ggbetweenstats(
data = questionnaireCAMs,
x = choosen_Robot,
y = mean_AlmereAnxiety
)
}
## mean_AlmereAttitude
tmp <- aov(formula = mean_AlmereAttitude ~ choosen_Robot, data = questionnaireCAMs)
if(summary(tmp)[[1]][["Pr(>F)"]][1] < .05){
ggbetweenstats(
data = questionnaireCAMs,
x = choosen_Robot,
y = mean_AlmereAttitude
)
}
## mean_LiWangAnthropomorphism
tmp <- aov(formula = mean_LiWangAnthropomorphism ~ choosen_Robot, data = questionnaireCAMs)
if(summary(tmp)[[1]][["Pr(>F)"]][1] < .05){
ggbetweenstats(
data = questionnaireCAMs,
x = choosen_Robot,
y = mean_LiWangAnthropomorphism
)
}
## mean_LiWangAutonomy
tmp <- aov(formula = mean_LiWangAutonomy ~ choosen_Robot, data = questionnaireCAMs)
if(summary(tmp)[[1]][["Pr(>F)"]][1] < .05){
ggbetweenstats(
data = questionnaireCAMs,
x = choosen_Robot,
y = mean_LiWangAutonomy
)
}
## mean_GAToRSpp
tmp <- aov(formula = mean_GAToRSpp ~ choosen_Robot, data = questionnaireCAMs)
if(summary(tmp)[[1]][["Pr(>F)"]][1] < .05){
ggbetweenstats(
data = questionnaireCAMs,
x = choosen_Robot,
y = mean_GAToRSpp
)
}
## mean_GAToRSpn
tmp <- aov(formula = mean_GAToRSpn ~ choosen_Robot, data = questionnaireCAMs)
if(summary(tmp)[[1]][["Pr(>F)"]][1] < .05){
ggbetweenstats(
data = questionnaireCAMs,
x = choosen_Robot,
y = mean_GAToRSpn
)
}
## mean_GAToRSsp
tmp <- aov(formula = mean_GAToRSsp ~ choosen_Robot, data = questionnaireCAMs)
if(summary(tmp)[[1]][["Pr(>F)"]][1] < .05){
ggbetweenstats(
data = questionnaireCAMs,
x = choosen_Robot,
y = mean_GAToRSsp
)
}
## mean_GAToRSsn
tmp <- aov(formula = mean_GAToRSsn ~ choosen_Robot, data = questionnaireCAMs)
if(summary(tmp)[[1]][["Pr(>F)"]][1] < .05){
ggbetweenstats(
data = questionnaireCAMs,
x = choosen_Robot,
y = mean_GAToRSsn
)
}Check for mean differences of valence between robots
ggbetweenstats(
data = questionnaireCAMs,
x = choosen_Robot,
y = mean_valence_macro_pre
)
ggbetweenstats(
data = questionnaireCAMs,
x = choosen_Robot,
y = mean_valence_macro_post
)
plot(questionnaireCAMs$mean_AlmereAttitude, questionnaireCAMs$mean_valence_macro_pre)
cor(questionnaireCAMs$mean_AlmereAttitude, questionnaireCAMs$mean_valence_macro_pre)[1] 0.4429144pre, post CAMs
# prepare data
### add pre post
networkIndicators_pre$timepoint <- "pre"
networkIndicators_post$timepoint <- "post"
### long data format
networkIndicators_long <- rbind(networkIndicators_pre, networkIndicators_post)
### add ID
networkIndicators_long$ID <- c(1:(nrow(networkIndicators_long) / 2), 1:(nrow(networkIndicators_long) / 2))
### reformat variable
networkIndicators_long$timepoint <- factor(networkIndicators_long$timepoint,
levels = c("pre", "post"),
ordered = FALSE)
### add type robot
networkIndicators_long$typeRobot <- ifelse(test = !is.na(networkIndicators_long$valence_micro_Rettungsroboter), yes = "rescue robots", no = "socially assistive robots")
table(networkIndicators_long$typeRobot)
rescue robots socially assistive robots
234 198 table(questionnaireCAMs$choosen_Robot) * 2
Rettungsroboter sozialer Assistenzroboter
234 198 ### post - pre difference of robot -> average valence
# ! all type of changes
fit1 <- afex::aov_car(mean_valence_macro ~ timepoint*typeRobot + Error(ID / timepoint),
data = networkIndicators_long)Converting to factor: typeRobotContrasts set to contr.sum for the following variables: typeRobotfit1a <- afex::aov_ez(id = "ID", dv = "mean_valence_macro",
data = networkIndicators_long, between=c("typeRobot"), within=c("timepoint"))Converting to factor: typeRobot
Contrasts set to contr.sum for the following variables: typeRobot# partical eta squared
anova(fit1, es = "pes")Anova Table (Type 3 tests)
Response: mean_valence_macro
num Df den Df MSE F pes Pr(>F)
typeRobot 1 214 0.37225 17.8759 0.077092 3.493e-05 ***
timepoint 1 214 0.01935 16.5368 0.071732 6.706e-05 ***
typeRobot:timepoint 1 214 0.01935 0.9891 0.004601 0.3211
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# generalized eta squared
fit1a # > identical resultsAnova Table (Type 3 tests)
Response: mean_valence_macro
Effect df MSE F ges p.value
1 typeRobot 1, 214 0.37 17.88 *** .074 <.001
2 timepoint 1, 214 0.02 16.54 *** .004 <.001
3 typeRobot:timepoint 1, 214 0.02 0.99 <.001 .321
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1dfvalcor <- data_summary(networkIndicators_long, varname="mean_valence_macro",
groupnames=c("timepoint","typeRobot"))Lade nötiges Paket: plyrWarning: Paket 'plyr' wurde unter R Version 4.3.2 erstellt------------------------------------------------------------------------------You have loaded plyr after dplyr - this is likely to cause problems.
If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
library(plyr); library(dplyr)------------------------------------------------------------------------------
Attache Paket: 'plyr'Die folgenden Objekte sind maskiert von 'package:dplyr':
arrange, count, desc, failwith, id, mutate, rename, summarise,
summarizeDas folgende Objekt ist maskiert 'package:purrr':
compactdfvalcor$timepoint <- factor(dfvalcor$timepoint, levels = c("pre", "post"))
p <- ggplot(dfvalcor, aes(x=timepoint, y=mean_valence_macro, fill=typeRobot)) +
geom_bar(stat="identity", color="black",
position=position_dodge()) +
geom_errorbar(aes(ymin=mean_valence_macro-se, ymax=mean_valence_macro+se), width=.2,
position=position_dodge(.9)) + ggplot_theme + ylab(label = "average emotional evaluation") +
theme(axis.title.x = element_text(size=20), axis.title.y = element_text(size=20), axis.text.x = element_text(size = 18), axis.text.y = element_text(size = 18), legend.text = element_text(size=16), legend.title = element_text(size=18))
print(p)
### post - pre difference of robot -> average valence
# ! only type of change B, D
tmp_ids <- questionnaireCAMs$PROLIFIC_PID[questionnaireCAMs$typeChange %in% c("B", "D")]
networkIndicators_long_BD <- networkIndicators_long[networkIndicators_long$participantCAM %in% tmp_ids,]
dim(networkIndicators_long); dim(networkIndicators_long_BD)[1] 432 47[1] 358 47fit1 <- afex::aov_car(mean_valence_macro ~ timepoint*typeRobot + Error(ID / timepoint),
data = networkIndicators_long_BD)Converting to factor: typeRobotContrasts set to contr.sum for the following variables: typeRobotfit1a <- afex::aov_ez(id = "ID", dv = "mean_valence_macro",
data = networkIndicators_long_BD, between=c("typeRobot"), within=c("timepoint"))Converting to factor: typeRobot
Contrasts set to contr.sum for the following variables: typeRobot# partical eta squared
anova(fit1, es = "pes")Anova Table (Type 3 tests)
Response: mean_valence_macro
num Df den Df MSE F pes Pr(>F)
typeRobot 1 177 0.38914 15.0344 0.078290 0.0001486 ***
timepoint 1 177 0.02291 15.2898 0.079514 0.0001312 ***
typeRobot:timepoint 1 177 0.02291 1.1923 0.006691 0.2763455
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# generalized eta squared
fit1a # > identical resultsAnova Table (Type 3 tests)
Response: mean_valence_macro
Effect df MSE F ges p.value
1 typeRobot 1, 177 0.39 15.03 *** .074 <.001
2 timepoint 1, 177 0.02 15.29 *** .005 <.001
3 typeRobot:timepoint 1, 177 0.02 1.19 <.001 .276
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1dfvalcor <- data_summary(networkIndicators_long_BD, varname="mean_valence_macro",
groupnames=c("timepoint","typeRobot"))
dfvalcor$timepoint <- factor(dfvalcor$timepoint, levels = c("pre", "post"))
p <- ggplot(dfvalcor, aes(x=timepoint, y=mean_valence_macro, fill=typeRobot)) +
geom_bar(stat="identity", color="black",
position=position_dodge()) +
geom_errorbar(aes(ymin=mean_valence_macro-se, ymax=mean_valence_macro+se), width=.2,
position=position_dodge(.9)) + ggplot_theme + ylab(label = "average emotional evaluation") +
theme(axis.title.x = element_text(size=20), axis.title.y = element_text(size=20), axis.text.x = element_text(size = 18), axis.text.y = element_text(size = 18), legend.text = element_text(size=16), legend.title = element_text(size=18))
print(p)
### post - pre difference of robot -> number of concepts
# ! all type of changes
fit1 <- afex::aov_car(num_nodes_macro ~ timepoint*typeRobot + Error(ID / timepoint),
data = networkIndicators_long)Converting to factor: typeRobot
Contrasts set to contr.sum for the following variables: typeRobotfit1a <- afex::aov_ez(id = "ID", dv = "num_nodes_macro",
data = networkIndicators_long, between=c("typeRobot"), within=c("timepoint"))Converting to factor: typeRobot
Contrasts set to contr.sum for the following variables: typeRobot# partical eta squared
anova(fit1, es = "pes")Anova Table (Type 3 tests)
Response: num_nodes_macro
num Df den Df MSE F pes Pr(>F)
typeRobot 1 214 20.5675 1.5163 0.00704 0.2195
timepoint 1 214 2.5012 199.2052 0.48210 <2e-16 ***
typeRobot:timepoint 1 214 2.5012 0.0249 0.00012 0.8748
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# generalized eta squared
fit1a # > identical resultsAnova Table (Type 3 tests)
Response: num_nodes_macro
Effect df MSE F ges p.value
1 typeRobot 1, 214 20.57 1.52 .006 .220
2 timepoint 1, 214 2.50 199.21 *** .092 <.001
3 typeRobot:timepoint 1, 214 2.50 0.02 <.001 .875
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1dfvalcor <- data_summary(networkIndicators_long, varname="num_nodes_macro",
groupnames=c("timepoint","typeRobot"))
dfvalcor$timepoint <- factor(dfvalcor$timepoint, levels = c("pre", "post"))
p <- ggplot(dfvalcor, aes(x=timepoint, y=num_nodes_macro, fill=typeRobot)) +
geom_bar(stat="identity", color="black",
position=position_dodge()) +
geom_errorbar(aes(ymin=num_nodes_macro-se, ymax=num_nodes_macro+se), width=.2,
position=position_dodge(.9)) + ggplot_theme + ylab(label = "average number of concepts") +
theme(axis.title.x = element_text(size=20), axis.title.y = element_text(size=20), axis.text.x = element_text(size = 18), axis.text.y = element_text(size = 18), legend.text = element_text(size=16), legend.title = element_text(size=18))
print(p)
### post - pre difference of robot -> number of concepts
# ! only type of change B, D
fit1 <- afex::aov_car(num_nodes_macro ~ timepoint*typeRobot + Error(ID / timepoint),
data = networkIndicators_long_BD)Converting to factor: typeRobot
Contrasts set to contr.sum for the following variables: typeRobotfit1a <- afex::aov_ez(id = "ID", dv = "num_nodes_macro",
data = networkIndicators_long_BD, between=c("typeRobot"), within=c("timepoint"))Converting to factor: typeRobot
Contrasts set to contr.sum for the following variables: typeRobot# partical eta squared
anova(fit1, es = "pes")Anova Table (Type 3 tests)
Response: num_nodes_macro
num Df den Df MSE F pes Pr(>F)
typeRobot 1 177 21.7252 1.9233 0.01075 0.1672
timepoint 1 177 2.4198 249.6170 0.58511 <2e-16 ***
typeRobot:timepoint 1 177 2.4198 0.0021 0.00001 0.9632
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# generalized eta squared
fit1a # > identical resultsAnova Table (Type 3 tests)
Response: num_nodes_macro
Effect df MSE F ges p.value
1 typeRobot 1, 177 21.73 1.92 .010 .167
2 timepoint 1, 177 2.42 249.62 *** .124 <.001
3 typeRobot:timepoint 1, 177 2.42 0.00 <.001 .963
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1dfvalcor <- data_summary(networkIndicators_long_BD, varname="num_nodes_macro",
groupnames=c("timepoint","typeRobot"))
dfvalcor$timepoint <- factor(dfvalcor$timepoint, levels = c("pre", "post"))
p <- ggplot(dfvalcor, aes(x=timepoint, y=num_nodes_macro, fill=typeRobot)) +
geom_bar(stat="identity", color="black",
position=position_dodge()) +
geom_errorbar(aes(ymin=num_nodes_macro-se, ymax=num_nodes_macro+se), width=.2,
position=position_dodge(.9)) + ggplot_theme + ylab(label = "average number of concepts") +
theme(axis.title.x = element_text(size=20), axis.title.y = element_text(size=20), axis.text.x = element_text(size = 18), axis.text.y = element_text(size = 18), legend.text = element_text(size=16), legend.title = element_text(size=18))
print(p)
Qualitative
Bird’s eye view (agg. CAMs)
for both robots
sel_ids <- questionnaireCAMs$PROLIFIC_PID
tmp_nodes <- CAMfiles_combined[[1]]
tmp_nodes$text_summarized <- str_remove(string = tmp_nodes$text_summarized, pattern = "_positive$|_negative$|_neutral$|_ambivalent$")
tmp_nodes$text_summarized <- str_trim(string = tmp_nodes$text_summarized)
CAMaggregated <- aggregate_CAMs(dat_merged = CAMfiles_combined[[3]], dat_nodes = tmp_nodes,
ids_CAMs = sel_ids)[1] "aggregate_CAMs: using participant CAM ids"
processing 216 CAMs...
[1] "== participantCAM in drawnCAM"
[1] "text_summarized column identified"g = CAMaggregated[[2]]
g2 = simplify(CAMaggregated[[2]])
# plot(g2, edge.arrow.size=0.01,
# vertex.size=diag(CAMaggregated[[1]]) / max(diag(CAMaggregated[[1]]))*20)
E(g2)$weight = sapply(E(g2), function(e) {
length(all_shortest_paths(g, from=ends(g2, e)[1], to=ends(g2, e)[2])$res) } )
E(g2)$weight = E(g2)$weight / 2
# E(g2)$weight[E(g2)$weight == 1] <- NA
V(g2)$color[V(g2)$value <= .5 & V(g2)$value >= -.5] <- "yellow"
V(g2)$shape <- NA
V(g2)$shape <- ifelse(test = V(g2)$color == "yellow", yes = "square", no = "circle")
### > plot multiple times because of random layout
for(i in 1:5){
plot(g2, edge.arrow.size = 0,
layout=layout_nicely, vertex.frame.color="black", asp = .5, margin = -0.1,
vertex.size=diag(CAMaggregated[[1]]) / max(diag(CAMaggregated[[1]]))*10,
vertex.label.cex = .9,
edge.weight=2, edge.width=(E(g2)$weight/10))
}
for rescue robot
sel_ids <- questionnaireCAMs$PROLIFIC_PID[questionnaireCAMs$choosen_Robot == "Rettungsroboter"]
tmp_nodes <- CAMfiles_combined[[1]]
tmp_nodes$text_summarized <- str_remove(string = tmp_nodes$text_summarized, pattern = "_positive$|_negative$|_neutral$|_ambivalent$")
tmp_nodes$text_summarized <- str_trim(string = tmp_nodes$text_summarized)
CAMaggregated <- aggregate_CAMs(dat_merged = CAMfiles_combined[[3]], dat_nodes = tmp_nodes,
ids_CAMs = sel_ids)[1] "aggregate_CAMs: using participant CAM ids"
processing 117 CAMs...
[1] "provided participantCAM ID in drawnCAM"
[1] "text_summarized column identified"g = CAMaggregated[[2]]
g2 = simplify(CAMaggregated[[2]])
# plot(g2, edge.arrow.size=0.01,
# vertex.size=diag(CAMaggregated[[1]]) / max(diag(CAMaggregated[[1]]))*20)
E(g2)$weight = sapply(E(g2), function(e) {
length(all_shortest_paths(g, from=ends(g2, e)[1], to=ends(g2, e)[2])$res) } )
E(g2)$weight = E(g2)$weight / 2
# E(g2)$weight[E(g2)$weight == 1] <- NA
V(g2)$color[V(g2)$value <= .5 & V(g2)$value >= -.5] <- "yellow"
V(g2)$shape <- NA
V(g2)$shape <- ifelse(test = V(g2)$color == "yellow", yes = "square", no = "circle")
### > plot multiple times because of random layout
for(i in 1:5){
plot(g2, edge.arrow.size = 0,
layout=layout_nicely, vertex.frame.color="black", asp = .5, margin = -0.1,
vertex.size=diag(CAMaggregated[[1]]) / max(diag(CAMaggregated[[1]]))*15,
vertex.label.cex = .9,
edge.weight=2, edge.width=(E(g2)$weight/10))
}
for social assistance robot
sel_ids <- questionnaireCAMs$PROLIFIC_PID[questionnaireCAMs$choosen_Robot == "sozialer Assistenzroboter"]
tmp_nodes <- CAMfiles_combined[[1]]
tmp_nodes$text_summarized <- str_remove(string = tmp_nodes$text_summarized, pattern = "_positive$|_negative$|_neutral$|_ambivalent$")
tmp_nodes$text_summarized <- str_trim(string = tmp_nodes$text_summarized)
CAMaggregated <- aggregate_CAMs(dat_merged = CAMfiles_combined[[3]], dat_nodes = tmp_nodes,
ids_CAMs = sel_ids)[1] "aggregate_CAMs: using participant CAM ids"
processing 99 CAMs...
[1] "provided participantCAM ID in drawnCAM"
[1] "text_summarized column identified"g = CAMaggregated[[2]]
g2 = simplify(CAMaggregated[[2]])
# plot(g2, edge.arrow.size=0.01,
# vertex.size=diag(CAMaggregated[[1]]) / max(diag(CAMaggregated[[1]]))*20)
E(g2)$weight = sapply(E(g2), function(e) {
length(all_shortest_paths(g, from=ends(g2, e)[1], to=ends(g2, e)[2])$res) } )
E(g2)$weight = E(g2)$weight / 2
# E(g2)$weight[E(g2)$weight == 1] <- NA
V(g2)$color[V(g2)$value <= .5 & V(g2)$value >= -.5] <- "yellow"
V(g2)$shape <- NA
V(g2)$shape <- ifelse(test = V(g2)$color == "yellow", yes = "square", no = "circle")
### > plot multiple times because of random layout
for(i in 1:5){
plot(g2, edge.arrow.size = 0,
layout=layout_nicely, vertex.frame.color="black", asp = .5, margin = -0.1,
vertex.size=diag(CAMaggregated[[1]]) / max(diag(CAMaggregated[[1]]))*15,
vertex.label.cex = .9,
edge.weight=2, edge.width=(E(g2)$weight/10))
}
check for difference
#### ! pre / post IDs are equal!
dat_ids <- questionnaireCAMs[, c("PROLIFIC_PID", "choosen_Robot", "typeChange", "CAMpre", "CAMpost")]
dat_ids <- dat_ids[dat_ids$typeChange %in% c("B", "D"),]
CAMfiles_combined[[1]][CAMfiles_combined[[1]]$participantCAM %in% dat_ids$PROLIFIC_PID[1],] CAM participantCAM
1 67d88f73-7672-4027-98f5-6463b4edc1f7 643cff9260890a3b38e3130d
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id
1 979727d1-1c72-40a6-8af8-9bd570cf97c0
2 f984b2a7-1be8-4a07-867b-44efcf08ea3a
3 ea0ec36e-df11-4dd7-9408-06f32380cc34
4 a901a80e-45f6-40f5-a1bd-fe676aa389a2
5 48c64a31-436d-408f-a3ed-ea5ecd6ef786
6 a155c36e-ac86-43d1-acd2-299f81bd4935
7 5ded478e-670d-476b-a6e6-5b07a7e8281a
8 e3404d4b-5979-46b9-82b2-72e9ff5575f1
9 a74d51d0-7470-4d6f-8f69-eed2fd8dcc9b
10 d0f10a24-24f0-461e-96f8-55b4f0c0b4da
11 f016f24d-dbe6-4787-ad85-c67ddc9b881c
12 1ebb723f-b712-4f50-9f77-f1e3421cc8c2
13 eccf0c07-b4dd-4a81-8fc0-fb6495e7652f
14 ec66f438-8a1f-4573-948a-5494dd034dce
15 8e885c6f-19d2-426b-ac8f-c378920e5e3f
16 fb5e8d10-ab9f-4752-9057-5ccebeb63220
17 f389c750-f6a8-429b-b83b-c28d5cab9577
18 122089dc-db80-4652-b3be-792b18a20e6b
3466 9d3a20c7-b975-451f-8795-6f3d92f7dd85
3467 a0d5ebb5-4a35-4bfa-aec5-be0520713f20
3468 e48cdbd0-ce7f-4d7b-8f08-99af25554d13
3469 d0a30ab9-b6f8-447b-bb9c-198c43a58574
3470 4e17f18b-af70-409e-be3d-a92f6788443a
3471 ddd01b88-2efb-4fdd-9ad9-edc6a78a4ac7
3472 ba969c64-5ce1-4ca1-be27-d251f877c349
3473 c4c1c350-ff76-4b02-b97e-c3b6a88487ab
3474 8cd7bd7d-752d-4210-98a3-e5dfd0592f57
3475 3aaba679-0e2a-428c-91f8-f8d426350592
3476 be1a11da-f517-4e48-8278-77c96ecb94e8
3477 375f262a-5ef7-4f5b-95a8-927974da04ca
3478 4de298f2-f22b-409d-b293-36c9d3c787d5
3479 6c07c808-f0b1-42b4-8862-8592f417d186
3480 80937a30-04ec-4e0f-b7ed-2b33f42a4e3b
3481 ab1cc43b-3b27-4525-b9c5-0d97e4ae718c
3482 6b1a1649-ea04-409f-83e0-4cb852a15813
3483 16f2feab-7903-4115-bc36-397eae2b3054
3484 7f30d084-9b46-4ed7-850c-ea7c3ff59a24
3485 758eec82-d3aa-450a-9ef7-e79d3642176f
3486 2a539b0b-5a19-4a86-9cf1-cbcc713b14e8
3487 84a56d91-5e6f-4d2a-9c72-46fa0e6c1745
3489 4b2789a2-967a-4608-9de8-f9cbcd773d09
text value comment date
1 sozialer Assistenzroboter 0 2023-12-11 10:06:04
2 Nachteile -1 2023-12-11 10:06:04
3 Vorteile 1 2023-12-11 10:06:04
4 strukturierte Therapieabläufe 1 2023-12-11 10:06:34
5 günstige Arbeitskräfte 2 2023-12-11 10:07:12
6 Ständige Verfügbarkeit (zeitlich) 3 2023-12-11 10:07:38
7 förderung sozialer Interaktion 3 2023-12-11 10:08:15
8 bei sozialer Isolation 2 2023-12-11 10:09:31
9 bei speziellen Bedürfnissen 1 2023-12-11 10:10:00
10 keine menschliche Nähe -3 2023-12-11 10:10:27
11 verlernen von Zuneigung -1 2023-12-11 10:10:58
12 mangelnde Empathie -2 2023-12-11 10:11:15
13 weniger Arbeitsplätze -1 2023-12-11 10:11:52
14 Erwartungsänderung an Menschen -2 2023-12-11 10:12:25
15 keine physische Begleitung -1 2023-12-11 10:13:06
16 Kosten für Entwicklung -1 2023-12-11 10:14:59
17 Entwickler:innen erhalten Arbeitsplätze 1 2023-12-11 10:15:43
18 bedingunslose emotionale Begleitung 1 2023-12-11 10:16:23
3466 sozialer Assistenzroboter 0 2023-12-11 10:19:00
3467 Nachteile -1 2023-12-11 10:19:00
3468 Vorteile 1 2023-12-11 10:19:00
3469 strukturierte Therapieabläufe 1 2023-12-11 10:19:00
3470 günstige Arbeitskräfte 2 2023-12-11 10:19:00
3471 Ständige Verfügbarkeit (zeitlich) 3 2023-12-11 10:19:00
3472 förderung sozialer Interaktion 3 2023-12-11 10:19:00
3473 bei sozialer Isolation 2 2023-12-11 10:19:00
3474 bei speziellen Bedürfnissen 1 2023-12-11 10:19:00
3475 keine menschliche Nähe -3 2023-12-11 10:19:00
3476 verlernen von Zuneigung -1 2023-12-11 10:19:00
3477 mangelnde Empathie -2 2023-12-11 10:19:00
3478 weniger Arbeitsplätze -1 2023-12-11 10:19:00
3479 Erwartungsänderung an Menschen -2 2023-12-11 10:19:00
3480 keine physische Begleitung -1 2023-12-11 10:19:00
3481 Kosten für Entwicklung -1 2023-12-11 10:19:00
3482 Entwickler:innen erhalten Arbeitsplätze 1 2023-12-11 10:19:00
3483 bedingunslose emotionale Begleitung 1 2023-12-11 10:19:00
3484 physische Nähe möglcih 1 2023-12-11 10:19:22
3485 physische Nähe möglich 1 2023-12-11 10:19:49
3486 emotionale Abhängigkeit -1 2023-12-11 10:20:20
3487 (emotionale) beeinflussung möglcih -1 2023-12-11 10:21:00
3489 Ersatz für Haustier 1 2023-12-11 10:21:52
x_pos y_pos predefinedConcept isDraggable isDeletable isTextChangeable
1 650 400 TRUE 0 0 0
2 450 400 TRUE 0 0 0
3 850 400 TRUE 0 0 0
4 1126 470 FALSE 1 1 1
5 1107 315 FALSE 1 1 1
6 923 200 FALSE 1 1 1
7 887 580 FALSE 1 1 1
8 843 702 FALSE 1 1 1
9 966 712 FALSE 1 1 1
10 356 254 FALSE 1 1 1
11 228 164 FALSE 1 1 1
12 151 264 FALSE 1 1 1
13 180 441 FALSE 1 1 1
14 379 135 FALSE 1 1 1
15 314 580 FALSE 1 1 1
16 1247 176 FALSE 1 1 1
17 54 574 FALSE 1 1 1
18 199 687 FALSE 1 1 1
3466 650 400 TRUE 0 0 0
3467 450 400 TRUE 0 0 0
3468 850 400 TRUE 0 0 0
3469 1126 470 FALSE 1 1 1
3470 1107 315 FALSE 1 1 1
3471 923 200 FALSE 1 1 1
3472 887 580 FALSE 1 1 1
3473 843 702 FALSE 1 1 1
3474 966 712 FALSE 1 1 1
3475 356 254 FALSE 1 1 1
3476 228 164 FALSE 1 1 1
3477 151 264 FALSE 1 1 1
3478 180 441 FALSE 1 1 1
3479 379 135 FALSE 1 1 1
3480 314 580 FALSE 1 1 1
3481 1247 176 FALSE 1 1 1
3482 54 574 FALSE 1 1 1
3483 199 687 FALSE 1 1 1
3484 519 128 FALSE 1 1 1
3485 360 729 FALSE 1 1 1
3486 472 582 FALSE 1 1 1
3487 546 278 FALSE 1 1 1
3489 1052 588 FALSE 1 1 1
isActive text_summarized
1 TRUE Soziale Assistenzroboter_neutral
2 TRUE Nachteile_negative
3 TRUE Vorteile_positive
4 TRUE RCPP_positive
5 TRUE LC_positive
6 TRUE T_positive
7 TRUE SIP_positive
8 TRUE SIP_positive
9 TRUE HRIP_positive
10 TRUE AN_negative
11 TRUE SIN_negative
12 TRUE AN_negative
13 TRUE SIN_negative
14 TRUE SIN_negative
15 TRUE R_negative
16 TRUE HC_negative
17 TRUE SIP_positive
18 TRUE T_positive
3466 TRUE Soziale Assistenzroboter_neutral
3467 TRUE Nachteile_negative
3468 TRUE Vorteile_positive
3469 TRUE RCPP_positive
3470 TRUE LC_positive
3471 TRUE T_positive
3472 TRUE SIP_positive
3473 TRUE SIP_positive
3474 TRUE HRIP_positive
3475 TRUE AN_negative
3476 TRUE SIN_negative
3477 TRUE AN_negative
3478 TRUE SIN_negative
3479 TRUE SIN_negative
3480 TRUE R_negative
3481 TRUE HC_negative
3482 TRUE SIP_positive
3483 TRUE T_positive
3484 TRUE AP_positive
3485 TRUE AP_positive
3486 TRUE HRIN_negative
3487 TRUE HRIN_negative
3489 TRUE RCPP_positiveopen text answers (adaptive question)
Question: Ihre angepasste Mind-Map hatte eine durchschnittliche emotionale Bewertung von XXX, diese war im Vergleich zu ihrer anfangs gezeichneten Mind-Map (durchschnittliche emotionale Bewertung von XXX) XXX. Bitte erklären Sie, warum Sie diese XXX wahrgenommen haben:
questionnaireCAMs$meanDifferencesCAMs <- round(x = questionnaireCAMs$mean_valence_macro_post - questionnaireCAMs$mean_valence_macro_pre, digits = 2)
DT::datatable(questionnaireCAMs[,c("meanDifferencesCAMs", "adaptiveQuestion", "feedback_critic")], options = list(pageLength = 5)) hist(questionnaireCAMs$meanDifferencesCAMs)
summary(questionnaireCAMs$meanDifferencesCAMs) Min. 1st Qu. Median Mean 3rd Qu. Max.
-0.42000 -0.05000 0.01500 0.05352 0.15000 1.21000 Only for people who have added new concpets (typ B, D)
tmp <- questionnaireCAMs[questionnaireCAMs$PROLIFIC_PID %in% dat_ids$PROLIFIC_PID, ]
DT::datatable(tmp[,c("meanDifferencesCAMs", "adaptiveQuestion", "feedback_critic")], options = list(pageLength = 5)) hist(tmp$meanDifferencesCAMs)
summary(tmp$meanDifferencesCAMs) Min. 1st Qu. Median Mean 3rd Qu. Max.
-0.42000 -0.08000 0.05000 0.06128 0.18500 1.21000 check for words
for(i in 1:nrow(dat_ids)){
}